A common mode of electric power supply and demand between electric power companies and clients is to sign agreements related to the electric power consumption between the electric power companies and the clients. Such an agreement mainly specifies that, if a total power consumption consumed by a client within a specific time interval (e.g., within fifteen minutes or half an hour) does not exceed an upper limit of power consumption as specified in the agreement, the client can pay the electric bill at a favorable rate. Conversely, if the total power consumption consumed by the client within the specific time interval exceeds the upper limit of power consumption, then the client may have to pay the electric bill at a higher rate.
Accordingly, immense efforts have to be made by the clients to accurately predict the total power consumption within a specific time interval during the power consumption process so that power consumption of electric power equipment can be adjusted in real time by the clients to avoid excessive expenditure caused when the total power consumption exceeds the agreed upper limit.
However, conventional technologies for predicting the total power consumption are mostly developed for specific electric power usage environments. Therefore, when the electric power usage environment of a client changes, the technology originally used for predicting the total power consumption will be unable to change adaptively, which makes the predicted total power consumption significantly different from the actual total power consumption. As a result, a false determination may be made by the client on whether it is necessary to adjust the power consumption mode of the electric power equipment, and this increases the probability of violating the agreement or decreases the utilization factor of the electric power equipment.
Accordingly, an urgent need exists in the art to provide a solution capable of dynamically adjusting the approach of predicting a total power consumption in response to possible changes in power usage environment of the clients so as to remarkably improve accuracy of the predicted total power consumption.